No abstract

No abstract

No abstract

The clinical high-risk period before a first episode of psychosis (CHR-P) has been widely studied with the goal of understanding the development of psychosis; however, less attention has been paid to the 75%–80% of CHR-P individuals who do not transition to psychosis. It is an open question whether multivariable models could be developed to predict remission outcomes at the same level of performance and generalizability as those that predict conversion to psychosis. Participants were drawn from the North American Prodrome Longitudinal Study (NAPLS3). An empirically derived set of clinical and demographic predictor variables were selected with elastic net regularization and were included in a gradient boosting machine algorithm to predict prodromal symptom remission. The predictive model was tested in a comparably sized independent sample (NAPLS2). The classification algorithm developed in NAPLS3 achieved an area under the curve of 0.66 (0.60–0.72) with a sensitivity of 0.68 and specificity of 0.53 when tested in an independent external sample (NAPLS2). Overall, future remitters had lower baseline prodromal symptoms than nonremitters. This study is the first to use a data-driven machine-learning approach to assess clinical and demographic predictors of symptomatic remission in individuals who do not convert to psychosis. The predictive power of the models in this study suggest that remission represents a unique clinical phenomenon. Further study is warranted to best understand factors contributing to resilience and recovery from the CHR-P state.

Prediction and prevention of negative clinical and functional outcomes represent the two primary objectives of research conducted within the clinical high-risk for psychosis (CHR-P) paradigm. Several multivariable “risk calculator” models have been developed to predict the likelihood of developing psychosis, although these models have not been translated to clinical use. Overall, less progress has been made in developing effective interventions. In this paper, we review the existing literature on both prediction and prevention in the CHR-P paradigm and, primarily, outline ways in which expanding and combining these paths of inquiry could lead to a greater improvement in individual outcomes for those most at risk.

Background Recent research has identified a number of pre-traumatic, peri-traumatic and post-traumatic psychological and ecological factors that put an individual at increased risk for developing PTSD following a life-threatening event. While these factors have been found to be associated with PTSD in univariate analyses, the complex interactions of these risk factors and how they contribute to individual trajectories of the illness are not yet well understood. In this study, we examine the impact of prior trauma, psychopathology, sociodemographic characteristics, community and environmental information, on PTSD onset in a nationally representative sample of adults in the United States, using machine learning methods to establish the relative contributions of each variable. Methods Individual risk factors identified in Waves 1 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) were combined with community-level data for the years concurrent to the NESARC Wave 1 (n = 43,093) and 2 (n = 34,653) surveys. Machine learning feature selection and classification analyses were used at the national level to create models using individual- and community-level variables that would best predict the new onset of PTSD at Wave 2. Results Our classification algorithms yielded 89.7 to 95.6% accuracy for predicting new onset of PTSD at Wave 2. A prior diagnosis of DSM-IV-TR Borderline Personality Disorder, Major Depressive Disorder or Anxiety Disorder conferred the greatest relative influence in new diagnosis of PTSD. Distal risk factors such as prior psychiatric diagnosis accounted for significantly greater relative risk than proximal factors (such as adverse event exposure). Conclusions Our findings show that a machine learning classification approach can successfully integrate large numbers of known risk factors for PTSD into stronger models that account for high-dimensional interactions and collinearity between variables. We discuss the implications of these findings as pertaining to the targeted mobilization emergency mental health resources. These findings also inform the creation of a more comprehensive risk assessment profile to the likelihood of developing PTSD following an extremely adverse event.

No abstract

Aim Recent findings suggest that family‐focused therapy (FFT) is effective for individuals at clinical high‐risk for psychosis (CHR‐P). As outcomes of CHR‐P individuals are quite varied, certain psychosocial interventions may be differentially effective in subgroups. The present study examined change in positive symptoms for CHR‐P individuals at different levels of predicted risk for conversion to psychosis who received either FFT, a brief form of family education termed enhanced care (EC) or treatment as usual. Methods Participants were drawn from the North American Prodromal Longitudinal Study (NAPLS2). A subset of NAPLS2 participants completed a randomized study involving FFT or EC. The present study includes participants from the FFT‐CHR sub‐study and non‐randomized NAPLS2 participants. Predicted risk of conversion was calculated using the Individualized Risk Calculator for Psychosis. Robust linear regressions evaluated whether the association between predicted risk of conversion and positive symptom change differed across intervention groups. Results A total of 94 participants from the FFT‐CHR sub‐study (FFT‐CHR n = 50, EC n = 44) and 401 non‐randomized NAPLS2 participants were included in this study. There was a treatment group by predicted risk of conversion interaction that predicted positive symptom improvement: higher risk individuals improved more with FFT‐CHR than EC or the non‐randomized NAPLS group, whereas lower‐risk individuals did not differ in positive symptom improvement across treatment groups (FFT‐CHR vs EC: P = .03, β = 20.27; FFT‐CHR vs NAPLS2: P < .001, β = 28.40). Conclusions Intensive treatments such as FFT‐CHR may be most appropriate for individuals at the highest levels of clinical risk for psychosis.